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Research Article
Base-substitution rates of nuclear and mitochondrial genes for polyclad flatworms
expand article infoDaniel Cuadrado‡, Jorge Rodríguez§|, Annie Machordom‡, Carolina Noreña‡, Fernando Á. Fernández-Álvarez, Pat A. Hutchings§|, Jane E. Williamson|
‡ National Museum of Natural Science, Madrid, Spain
§ Australian Museum, Sydney, Australia
| Macquarie University, Sydney, Australia
¶ Institut de Ciències del Mar, Barcelona, Spain

Corresponding author: Daniel Cuadrado ( cuadradopm@hotmail.com )

Academic editor: Pavel Stoev
© 2024 Daniel Cuadrado, Jorge Rodríguez, Annie Machordom, Carolina Noreña, Fernando Á. Fernández-Álvarez, Pat A. Hutchings, Jane E. Williamson.
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation: Cuadrado D, Rodríguez J, Machordom A, Noreña C, Fernández-Álvarez FÁ, Hutchings PA, Williamson JE (2024) Base-substitution rates of nuclear and mitochondrial genes for polyclad flatworms. Zoosystematics and Evolution 100(3): 863-876. https://doi.org/10.3897/zse.100.119945
ZooBank: urn:lsid:zoobank.org:pub:653F388E-73B8-4CA4-BB1E-0C7262D170D7
Open Access

Abstract

The increase in the use of molecular methodologies in systematics has driven the necessity for a comprehensive understanding of the limitations of different genetic markers. Not every marker is optimal for all species, which has led to multiple approaches in the study of the taxonomy and phylogeny of polyclad flatworms. The present study evaluates base-substitution rates of nuclear ribosomal (18S rDNA and 28S rDNA), mitochondrial ribosomal (16S rDNA), and protein-codifying (cytb, cox1) markers for this taxonomic group, with the main objective of assessing the robustness of these different markers for phylogenetic studies. Mutation rates and Ti/Tv ratios of the other markers were assessed for the first time. We estimated substitution rates and found cytb to be the most variable, while 18S rDNA was the least variable among them. On the other hand, the transition to transversion (Ti/Tv) ratio of the different genes revealed differences between the markers, with a higher number of transitions in the nuclear gene 28S and a higher number of transversions in the mitochondrial genes. Lastly, we identified that the third codon position of the studied protein-codifying genes was highly variable and that this position was saturated in the cox1 marker but not in cytb. We conclude that it is important to assess the markers employed for different phylogenetic levels for future studies, particularly in the order Polycladida. We encourage the use of mitochondrial genes cytb and 16S for phylogenetic studies at suborder, superfamily, and family levels and species delimitation in polyclads, in addition to the well-known 28S and cox1.

Key Words

Acotylea, codon, Cotylea, entropy, flatworm, molecular, purines, pyrimidines, saturation

Introduction

Fast and reliable DNA sequencing has become a routinely used methodology in the description and barcoding of new species. In particular, a fragment of the mitochondrial gene cytochrome oxidase c subunit 1 (cox1) has become the most frequently used marker for molecular identification-based DNA barcoding (Hebert et al. 2003) in the majority of species across all taxa, incentivised by the Barcode of Life initiative (www.barcodeoflife.org) (Ratnasingham and Hebert 2007). Recent studies show, however, that genome-wide nucleotide substitution patterns in coding sequences have species-specific features and are variable among evolutionary lineages (Zou and Zhang 2021), leading to the question of the ubiquity of their use of particular nuclear and mitochondrial genes for systematics.

To address this issue, we investigated transition bias, which involves analysing the frequency and nature of nucleotide changes between purines and pyrimidines across species genomes. This information is crucial for understanding the behaviour of different markers commonly employed in phylogenetic studies. Nucleotide changes between purines (adenine, A, and guanine, G) and pyrimidines (cytosine, C, and thymine, T) are known as transitions, whereas changes between a purine and a pyrimidine are coined transversions. Due to the disparity in the number of types of each possible nucleotide change (four types of transitions compared to eight types of transversions), the expected number of transitions relative to that of transversions (Ti/Tv ratio) would be 0.5 in DNA sequence evolution, assuming all types of nucleotide changes had equal rates of occurrence. However, Ti/Tv often exceeds 0.5 or even 1, a phenomenon known as transition bias (Nei and Kumar 2000; Yang 2006). Ti/Tv bias is commonly considered for estimating nucleotide substitution rates, inferring molecular phylogenies, and testing for natural selection (Kimura 1980; Tamura and Nei 1993; Yang et al. 1998) and has been extensively studied in model organisms such as the yeast Saccharomyces cerevisiae (Liu and Zhang 2019), the common fruit fly Drosophila melanogaster (Schrider et al. 2013), the flowering plant Arabidopsis thaliana (Ossowski et al. 2010), and the nematode Caenorhabditis elegans (Denver et al. 2009). These studies suggest that transitions are less deleterious and less likely to be purged by natural selection than transversions, which could be a reason why transitions are more commonly found. Furthermore, studies on genome error correction show that, due to the structure of the genetic code, transversions often lead to non-synonymous mutations compared to transitions, which usually lead to synonymous mutations, thereby potentially affecting the function and phenotype of the encoded proteins (Zhang 2000; Schrider et al. 2013). Therefore, while transitions are more frequent than transversions, especially at lower taxonomic levels, transversions are considered less informative and more difficult to interpret, potentially leading to homoplasy effects (evolutionary convergence) when comparing distantly related species in parsimony-based phylogenies (Broughton et al. 2000).

Understanding relationships among closely related taxa at a species level is essential for conserving biodiversity, maintaining ecosystem functioning, and understanding macroevolutionary processes (Oliver et al. 2015). External morphological characteristics are historically used as diagnostic features for species identification; however, contrasting results among morphological and molecular analyses appear across the entire animal kingdom, including nemerteans (Strand and Sundberg 2005), corals (Forsman et al. 2009), molluscs (Valdés et al. 2017; Fernández-Álvarez et al. 2020), polychaetes (Kupriyanova et al. 2023), fish (Park et al. 2020), insects (Selivon et al. 2005; Zhang et al. 2021), and also flatworms (Litvaitis et al. 2019).

Flatworms (order Polycladida) are free-living, carnivorous organisms that occur in a diversity of marine habitats, with over 800 species described worldwide (Tyler et al. 2006–2024). Exploring the diversity of polyclad species is critical, considering recent studies indicating the importance of the chemical and ecological roles of flatworms (Rawlinson and Stella 2012; Gammoudi et al. 2016; McNab et al. 2021, 2022; Tosetto et al. 2023). Traditionally, the taxonomy and phylogenetics of the order Polycladida have been based on morphological characteristics, where differences in tentacles, eyespots, ventral sucker, and genitalia are used to classify polyclads into different genera and families (Faubel 1983, 1984; Prudhoe 1985). External morphological characters are, however, not always an accurate reflection of the evolutionary relationships in flatworms. For example, different families of Leptoplanoidea (Acotylea) display very similar external morphologies but show different and distinguishable features internally and molecularly (Bahia 2016; Dittmann et al. 2019). Sometimes species with few morphological differences show large molecular discrepancies (Carrera-Parra et al. 2011), and the problem is exacerbated when different cryptic polyclad species live in sympatry, thereby complicating accurate identification and potentially resulting in the amalgamation of multiple species into a single one. It is therefore important to identify which molecular markers are best suited to resolving the evolutionary lineages of flatworms.

A variety of molecular markers have been used to date for the systematic analysis of polyclads. Resolution of deep nodes such as suborders (Cotylea and Acotylea) and assessment of differences in superfamilies and families have initially been based on the 28S rDNA marker (Litvaitis and Newman 2001; Litvaitis et al. 2010; Rawlinson et al. 2011; Bahia et al. 2017; Cuadrado et al. 2021). Recent studies have, however, noted deficiencies in this marker (Dittmann et al. 2019; Litvaitis et al. 2019), because only a section of the phylogenetic tree topologies in Cotylea is consistently reconstructed. In the case of suborder Acotylea, despite recent studies (Oya and Kajihara 2020), there is a need for the inclusion of more taxa, additional genetic markers, complete markers, and/or searching for other alternatives to enhance understanding.

Other polyclad studies have used a range of different molecular markers, often employing specific primers due to performance issues with universal primers, such as cox1, the 16S mitochondrial ribosomal subunit (16S rDNA), the mitochondrial cytochrome b (cytb), and the nuclear 18S rDNA (Vella et al. 2016; Aguado et al. 2017; Tsunashima et al. 2017; Oya and Kajihara 2017, 2020; Oya et al. 2019; Tsuyuki et al. 2019, 2022; Cuadrado et al. 2021; Rodríguez et al. 2021), as well as complete mitochondrial genomes (Aguado et al. 2016; Kenny et al. 2019; Yonezawa et al. 2020) for both systematics and species delimitation.

This study evaluates the strength of support provided by cox1, 16S rRNA, and cytb mitochondrial genes, as well as the 18S rDNA and 28S rDNA nuclear genes, on the phylogeny of the Polycladida through the study of nucleotide substitutions.

Materials and methods

Sampling sites and processing of materials

Polyclad flatworms were collected from different sites along the coasts of eastern Australia, the Iberian Peninsula, the Canary Islands, Cape Verde, Costa Rica, Cyprus, and Martinique Island (Table 2). This broad distribution range included representation of the majority of superfamilies across the order Polycladida, including Pseudocerotoidea Faubel, 1984; Prosthiostomoidea Bahia, Padula, & Schrödl, 2017 for the suborder Cotylea; Leptoplanoidea Faubel, 1984; Stylochoidea Poche, 1925; and Discoceloidea Laidlaw, 1903 for the Acotylea suborder. These species stem from a compilation of available biological material from recent studies (Noreña et al. 2014, 2015; Marquina et al. 2015a, 2015b; Aguado et al. 2017; Pérez-García et al. 2019; Cuadrado et al. 2021; Rodríguez et al. 2021; Soutullo et al. 2021), with the aim of achieving the greatest possible representativeness and sequencing of all available samples.

Flatworms were collected from under rocks in coastal environments, either by hand for intertidal and shallow individuals or using SCUBA in deeper areas, and placed in separate containers filled with seawater (specific information on species is available in the bibliography of Table 2). After being transported to a laboratory, a small piece of tissue (<1 g) was removed from the body margin of each individual using a sterile scalpel blade. The tissue of each animal was fixed in absolute ethanol and stored for DNA extraction. Each animal was then coaxed onto a piece of paper and transferred to a Petri dish containing clean, frozen seawater, where it was fixed with either 10% formalin or Bouin’s liquid. Once the fixation process was complete, specimens were stored in 70% ethanol for species identification through morphological techniques, as per Rodríguez et al. (2021).

DNA extraction and amplification

Total genomic DNA was extracted from each tissue sample using an Isolate II Genomic DNA Kit (Meridian Bioscience®) following the manufacturer’s protocol. Amplicons from two nuclear (28S rDNA, 18S rDNA) and three mitochondrial (16S rRNA, cox1, and cytb) target genes from each polyclad species were sequenced. All polymerase chain reactions (PCRs) were performed using Taq DNA polymerase (Qiagen). The reaction mix included: H2O – 10.92 μl; 10x buffer − 2 μl; 25 mM MgCl2 − 4 μl; 0.5 mM dNTP − 1 μl; 10 μM primer – 0.25 μl /primer; Taq 5 U/μl − 0.08 μl; DNA – 1.5 μl. This gave a reaction volume of 20 μl.

Sequences of approximately 1100 base pairs (bp) (28S), 800 pb (18S), 500 bp (16S), 1000 bp (cox1), and 400 bp (cytb) were amplified using the primers listed in Table 1. The PCR consisted of an initial denaturation at 95 °C for 3 min, followed by 40 cycles of denaturation at 95 °C for 1 min, annealing at 47 °C (cytb), 49 °C (cox1), 59 °C (28S rDNA, 18S rDNA, 16S rRNA) for 30 sec, and extension at 72 °C for 1 min, with a final extension of 10 min at 72 °C.

Table 1.
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Primers used in this study.

Gene Primer name Sequence Reference
18S 18SF2 ACTTTGAACAAATTTGAGTGCTCA Morgan et al. (2003)
1800mod GATCCTTCCGCAGGTTCACCTACG Raupach et al. (2009)
28S Platy28S_F AGCCCAGCACCGAATCCT Cuadrado et al. (2021)
Platy28S_R GCAAACCAAGTAGGGTGTCGC Cuadrado et al. (2021)
16S PLATYS16SF1 ACAACTGTTTATCAAAAACAT Aguado et al. (2017)
PLATYS16SR1 ACGCCGGTYTTAACTCAAATCA Aguado et al. (2017)
cox1 HRpra2 AATAAGTATCATGTARACTDATRTCT Tsunashima et al. (2017)
HRprb2-2 GDGGVTTTGGDAATTGAYTAATACCTT Tsunashima et al. (2017)
Acotylea_COI_F ACTTTATTCTACTAATCATAAGGATATAGG Oya and Kajihara (2017)
Acotylea_COI_R CTTTCCTCTATAAAATGTTACTATTTGAGA Oya and Kajihara (2017)
cytb cytb424-444 CAGGAAACAGCTATGACCGGWTAYGTWYTWCCWTGRGGWCARAT Jondelius et al. (2002)
cytb876-847 TGTAAAACGACGGCCAGTGCRTAWGCRAAWARRAARTAYCAYTCWGG Jondelius et al. (2002)

The PCR products were observed using TBE gel electrophoresis in 1.5% agarose gel stained with SYBER Safe and visualised under UV light. PCR products were sent to Macrogen Korea for clean-up and sequencing. Lastly, the obtained forward and reverse sequences were combined using the programme Geneious Prime 2020.2.4 (http://www.geneious.com, Kearse et al. 2012) using the alignment-transition/transversion with the consensus sequence tool and manually curated.

The species with the highest possible number of correctly sequenced genes was selected to compare the analyses performed on the different markers. All sequences obtained in the present study have been deposited in the GenBank database under the accession numbers listed in Table 2.

Table 2.
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List of species and sequences studied (material from previous studies, see table list of references).

Family Species 18S 28S 16S cox1 cytb Locality Reference
Discoceloidea
Cryptocelidae Cryptocelis sp. MZ292810 MZ292829 MZ292858 MZ273073 PP856191 Galicia, Spain Noreña et al. (2015)
Discocelidae Discocelis tigrina MZ292799 MK299370 - - PP856182 Cuadrado et al. (2021)
Leptoplanoidea
Gnesiocerotidae Echinoplana celerrima MW376754 MW377507 MW376599 MW375911 MW392971 New South Wales, Australia Rodríguez et al. (2021)
Ceratoplana falconerae MW376740 MW377493 MW376585 MW375897 MW392973 Victoria, Australia Rodríguez et al. (2021)
Parabolia megae MW376744 MW377497 MW376589 MW375901 MW392974 New South Wales, Australia Rodríguez et al. (2021)
Leptoplanidae Leptoplana sp. - MZ292828 MZ292853 MZ273072 - Cape Verde Island Cuadrado et al. (2021)
Parviplana geronimoi MZ292807 - MZ292855 - - Cádiz, Spain Pérez-García et al. (2019)
Notoplanidae Notoplana australis MW376750 MW377503 MW376595 MW375907 MW392986 New South Wales, Australia Rodríguez et al. (2021)
Notoplana felis MW376753 MW377506 MW376598 MW375910 MW392985 Victoria, Australia Rodríguez et al. (2021)
Pleioplanidae Pleioplana atomata MZ292820 MZ292832 MZ292866 MZ273074 PP856198 Asturias, Spain Marquina et al. (2015a)
Pleioplana sp. MZ292808 MZ292840 MZ292856 MZ273079 PP856189 Cádiz, Spain This study
Pseudostylochidae Tripylocelis typica MW376752 MW377505 MW376597 MW375909 MW392983 New South Wales, Australia Rodríguez et al. (2021)
Stylochoplanidae Stylochoplana clara MW376741 MW377494 MW376586 MW375898 MW392972 Victoria, Australia Rodríguez et al. (2021)
Stylochoidea
Callioplanidae Callioplana marginata MW376747 MW377500 MW376592 MW375904 MW392984 New South Wales, Australia Rodríguez et al. (2021)
Neostylochus ancorus MW376748 MW377501 MW376593 MW375905 - New South Wales, Australia Rodríguez et al. (2021)
Latocestidae Eulatocestus australis MW376749 MW377502 MW376594 MW375906 - New South Wales, Australia Rodríguez et al. (2021)
Latocestus plehni MZ292806 MK299376 MZ292852 - PP856187 Cape Verde Island Cuadrado et al. (2021)
Planoceridae Paraplanocera marginata MW376745 MW377498 MW376590 MW375902 MW392981 New South Wales, Australia Rodríguez et al. (2021)
Paraplanocera sp. MZ292818 MZ292833 MZ292868 MZ273075 PP856200 Cyprus This study
Planocera edmondsi MW376755 MW377508 MW376600 MW375912 MW392979 Victoria, Australia Rodríguez et al. (2021)
Planocera pellucida MZ292797 MK299355 - - PP856180 Canary Island, Spain Cuadrado et al. (2021)
Idioplanidae Idioplana australiensis MW376746 MW377499 MW376591 MW375903 MW392980 New South Wales, Australia Rodríguez et al. (2021)
Stylochidae Imogine fafai MZ292817 MZ292835 MZ292865 MF371138 PP856197 Asturias, Spain Aguado et al. (2017)
Leptostylochus victoriensis MW376742 MW377495 MW376587 MW375899 MW392982 New South Wales, Australia Rodríguez et al. (2021)
Stylochus neapolitanus MZ292800 MZ292841 MZ292846 MF371141 PP856183 Galicia, Spain Aguado et al. (2017)
Boninioidea
Boniniidae Boninia sp. MZ292819 MZ292834 MZ292869 - PP856201 Costa Rica Soutullo et al. (2021)
Cestoplanidae Cestoplana rubrocincta MW376751 MW377504 MW376596 MW375908 MW392977 New South Wales, Australia Rodríguez et al. (2021)
Pericelidae Pericelis beyerleyana MZ292801 MK299374 MZ292847 - PP856184 Martinique Island Cuadrado et al. (2021)
Pericelis cata MZ292805 MK299352 MZ292851 - - Cape Verde Island Cuadrado et al. (2021)
Prosthiostomoidea
Prosthiostomidae Prosthiostomum amri MW376743 MW377496 MW376588 MW375900 MW392978 New South Wales, Australia Rodríguez et al. (2021)
Prosthiostomum siphunculus MZ292816 MZ292836 MZ292864 MZ273080 PP856196 Almuñécar, Spain Pérez-García et al. (2019)
Prosthiostomum sp. MZ292795 MZ292826 MZ292842 MZ273071 - New South Wales, Australia Rodriguez et al. (2021)
Enchiridium magec - MK299349 MZ292844 - PP856179 Canary Island, Spain Cuadrado et al. (2021)
Pseudocerotoidea
Euryleptidae Eurylepta cornuta MZ292809 MZ292839 MZ292857 MF371139 PP856190 Galicia, Spain Aguado et al. (2017)
Eurylepta guayota MZ292804 MK299372 MZ292850 - PP856186 Martinique Island Cuadrado et al. (2021)
Prostheceraeus roseus MZ292811 KY263688 MZ292859 MZ273078 PP856192 Galicia, Spain Noreña et al. (2014)
Pseudocerotidae Phrikoceros sp. MZ292796 MZ292827 MZ292843 - PP856178 Victoria, Australia Rodriguez et al. (2021)
Pseudoceros depiliktabub MZ292813 MZ292837 MZ292861 - PP856194 Lizard Island, Australia Marquina et al. (2015b)
Pseudoceros stimpsoni MZ292812 MZ292838 MZ292860 MF371147 PP856193 Lizard Island, Australia Aguado et al. (2017)
Pseudoceros velutinus MZ292798 MK299381 MZ292845 MZ273076 PP856181 Canary Island, Spain Cuadrado et al. (2021)
Pseudoceros rawlinsonae var. galaxy - MK299357 MZ292854 - PP856188 Cape Verde Island Cuadrado et al. (2021)
Pseudobiceros flowersi MZ292814 MZ292830 MZ292862 - PP856195 Lizard Island, Australia Marquina et al. (2015b)
Pseudobiceros hymanae MZ292815 MZ292831 MZ292863 - - Lizard Island, Australia Marquina et al. (2015b)
Pseudobiceros caribbensis MZ292803 MK299378 MZ292849 MZ273077 PP856185 Martinique Island Cuadrado et al. (2021)
Thysanozoon alagoensis MZ292802 MK299383 MZ292848 - - Martinique Island Cuadrado et al. (2021)
Thysanozoon brocchii MW376738 MW377491 MW376583 - MW392976 Victoria, Australia Rodríguez et al. (2021)
Yungia aurantiaca - MK299386 MZ292867 - PP856199 Cádiz, Spain Cuadrado et al. (2021)

Comparison of genetic markers

Alignments of each molecular marker were performed with the Clustal W algorithm (Larkin et al. 2007) using the programme Geneious Prime 2020.2.4. Ambiguously aligned and variable regions were recognised and excluded using the programme Gblocks version 0.91b (Castresana 2000) with relaxed parameters (smaller final blocks). This resulted in matrices of 521 bp (cox1), 500 bp (16S rRNA), 393 bp (cytb), 1047 bp (28S rDNA), and 859 bp (18S rDNA).

A supplementary entropy analysis was also performed with IQ-TREE version 1.6.12 (Trifinopoulos et al. 2016) to quantify the genetic variability across the length of the obtained sequences and assess the grade of conservation of each marker (entropy estimation by site).

The saturation rate of the substitutions of each genetic marker was quantified through a transition (Ti) and transversion (Tv) saturation graph using PAUP* Version 4.0a (Build 166) (Swofford 2003), as well as the distribution of variable sites and grade of genetic variability by site along the genes’ matrices with an entropy analysis using DAMBE 5 (Xia 2013). Interspecific distances for each gene were calculated in Mega 6 (Tamura et al. 2013).

Maximum likelihood (ML) analysis was performed with IQ-TREE (Trifinopoulos et al. 2016). The optimal substitution model selected by the Bayesian information criterion (BIC) proposed by the ModelFinder (Kalyaanamoorthy et al. 2017) was GTR+F+I+G4 (16S rDNA, cox1), TIM+F+I+G4 (cytb), K2P+I (18S rDNA), and TIM3+F+I+G4 (28S rDNA). The consensus tree of 1000 standard bootstrap pseudo-replicates was selected and edited with iTOL version 4 (Letunic and Bork 2019). A node was considered well supported when the bootstrap value was 80% or greater. Phylogenies without outgroups have been analysed to avoid including inconsistencies since it was not possible to obtain a common outgroup for the five markers studied.

Results

Entropy estimation by site

Entropy analysis revealed genetic variability across the length of the obtained sequences and assessed the grade of conservation of each marker. The variable positions of each studied gene presented a continuous distribution, with substitutions unequally distributed in the nuclear genes. 18S rDNA presented 58 out of 859 (6.75% of the alignment) variable positions (37 parsimonies informative, PIs), while 28S rDNA presented 388 out of 1047 (37.0%) variable positions (306 PIs). 16S rDNA presented 322 out of 500 (64.4%) variable positions (286 PIs), while cytb presented 234 out of 393 (59.54%) variable positions (218 PIs), and cox1 presented 293 out of 521 (56.2%) variable positions (280 PIs) (Table 3, Fig. 1A).

Figure 1. 

Genomic analysis of the studied genes. A. Entropy estimation by site: The X-axis indicates the number of sequenced positions, and the Y-axis indicates the number of variations of each position; B. Estimation of substitution rates in absolute values: The X-axis displays the pairwise genetic distance between sample pairs; the Y-axis indicates the number of mutations in absolute values; C. Estimation of Ti/Tv in pairwise sequence comparisons: The X-axis shows the pairwise genetic distance between sample pairs, and the Y-axis shows the Ti/Tv proportion; D. Estimation of transitions and transversions in pairwise sequence comparisons: The X-axis indicates the pairwise genetic distance between sample pairs, and the Y-axis indicates the proportion of transitions and transversions.

Table 3.
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Genetic variability of the analysed sequences.

Gene Average distance (%) Min distance (%) Max distance (%) S Cs PIs
18S rDNA 1.37 0.00 3.14 859 58 (6.75%) 37
28S rDNA 11.21 0.00 18.71 1047 388 (37.0%) 306
16S rRNA 22.06 0.28 32.77 500 322 (64.4%) 286
cytb 26.86 0.00 34.40 393 234 (59.5%) 218
cox1 24.86 0.22 34.44 521 293 (56.2%) 280

The minimum distance was calculated as the minimum divergence of all sequences; the maximum distance was calculated as the maximum divergence of all sequences; Cs: number of constant sites; S: total number of sites in the matrix; and PIs: number of parsimony informative sites.

The variable sites of each codon position of the protein-codifying genes (cytb and cox1) were also assessed. The third codon position presented the highest values of interspecific maximum distances in both markers: 69.41% in cytb and 53.83% in cox1. On the other hand, the second codon position had the lowest values of maximum distances, with 16.66% in cytb and 13.42% in cox1 (Table 4).

Table 4.
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Genetic variability of the analysed sequences of cytb and cox1 by codon position.

Gene Average distance (%) Min distance (%) Max distance (%)
cytb first codon position 20.38 0.00 30.58
cytb second codon position 8.88 0.00 16.66
cytb third codon position 51.71 0.00 69.41
cox1 first codon position 16.25 0.65 30.06
cox1 second codon position 5.51 0.00 13.42
cox1 third codon position 53.83 2.02 53.83

The minimum distance was calculated as the minimum divergence of all sequences; and the maximum distance was calculated as the maximum divergence of all sequences.

Estimate of substitution rate in absolute values

A total of 1485 (18S rDNA), 2556 (28S rDNA), 1653 (16S rDNA), 1176 (cytb), and 703 (cox1) pairwise comparisons from 43 (18S rDNA), 46 (28S rDNA), 45 (16S rDNA), 39 (cytb), and 30 (cox1) species were performed. Fig. 1B shows the number of substitutions in absolute values (abs) plotted against the pairwise distance between each sample. All cases presented a linear growth following these equations:

18S rDNA:

y = - 16.054x2 + 858.37x – 8E-05

R2 = 1.0000

28S rDNA:

y = - 760.97x2 + 1123.5x – 3.5153

R2 = 0.9743

16S rDNA:

y = - 114.54x2 + 462.66x – 1.4178

R2 = 0.9766

cytb:

y = 30.873x2 + 366.5x + 0.3227

R2 = 0.8436

cox1:

y = - 43.538x2 + 524.81x + 0.1409

R2 = 0.9901

The coefficient of determination (R2) was close to 1 in most cases, indicating that all values were close to a linear progression except for the cytb mitochondrial gene (R2 = 0.84).

Estimates of the transition/transversion ratio (Ti/Tv) in pairwise sequence comparisons

The estimated Ti/Tv ratios plotted against the estimated sequence distances showed the Ti/Tv ratio plotted against the pairwise distance between each sample (Fig. 1C). Two differentiated regions can be observed: the first was a region where the number of transitions and transversions randomly appeared with great variation. Due to the short distances between phylogenetic closely related species and the different numbers of transversions and transitions that each pair presented, the estimation showed disparate values depending on the selected samples, predominating the number of transitions, as they are the most probable among closely related species. As the distance between species pairs increased, a second region where the values stabilised around 1 (where 1 indicates the same number of transversions and transitions) appeared. While the value was slightly higher than 1 in most cases (indicating a greater number of transitions over transversions), starting from pairwise distances greater than 20%, the number of transversions increased compared to that of transitions in the case of the 16S rDNA and cox1 mitochondrial genes. Meanwhile, 28S rDNA presented a Ti/Tv ratio between 1 and 2 at longer distances, indicating an overall higher number of transitions.

Estimates of transitions and transversions in pairwise sequence comparisons

Congruent with the results of the Ti/Tv ratio, the initial number of transitions was higher than that of transversions for all gene markers. However, the number of transversions was greater at higher distances across all markers, as observed in the graphs, except for 28S rDNA, where transitions remained higher (Fig. 1D).

Differences among the three codon positions were evident (Fig. 2). The first codon position displayed maximum distances of 30.58% for cytb and 30.06% for cox1, compared to the maximum distances for the second codon position (16.66% and 13.42%, respectively) and those of the third codon position (69.41% and 70.94%). In both markers, the overall number of transversions was higher than that of transitions, apart from the first codon position, where the number of transitions was always higher than that of transversions. The second codon position displayed a lower mutation rate at shorter distances. Lastly, the third codon position presented a higher number of overall mutations (both transitions and transversions), with a higher proportion of transversions in both markers; however, a decrease in transition in the cox1 gene was observed at pairwise distances higher than 25%.

Figure 2. 

Estimation of transitions and transversions for each codon position (from top to bottom: first (1), second (2), and third (3) codon positions) in cytb (A) and cox1 (B). The X-axis indicates the pairwise genetic distance between sample pairs, and the Y-axis indicates the proportion of transitions and transversions.

Maximum-likelihood phylogenetic analyses

The matrices employed to analyse substitution ratios provided the following phylogenetic results through a maximum-likelihood analysis performed for each gene (Figs 3, 4). The results obtained for each marker are:

Figure 3. 

Maximum-likelihood phylogenetic analysis of nuclear gene markers (18S and 28S).

Figure 4. 

Maximum-likelihood phylogenetic analysis of mitochondrial gene markers (16S, cox1, and cytb).

18S rDNA (Fig. 3): This marker showed the separation of the two suborders of Polycladida (Cotylea and Acotylea) with a bootstrap support (BS) of 97. The superfamily Pseudocerotidae (Cotylea) was highly supported (BS = 100), as were the genera Pseudoceros (BS = 80) and Pseudobiceros (BS = 79). The superfamilies and families of Acotylea appeared without strong support (BS < 70).

28S rDNA (Fig. 3): In this study, the two suborders were well supported (BS = 100). Within Cotylea, the four analysed superfamilies were well delimited and held: Periceloidea and Boninioidea produced two independent lineages, and Pseuderotoidea (BS = 94) and Prosthiostomoidea (BS = 100) were highly supported. Within the last superfamily Pseudocerotidae (including the genera Pseudoceros, Pseudobiceros, Thysanozoon, and Phrikoceros; BS = 98), an independent cluster for the family Euryleptidae was seen. Similarly, within the Acotylea suborder, the different superfamilies Leptoplanoidea and Stylochoidea showed high support values (BS = 100 and 88, respectively). Families such as Styochoplanidae (BS = 100), Leptoplanidae (BS = 99), Latocestidae (BS = 93), and Stylochidae (BS = 100) were also well supported.

16S rRNA (Fig. 4): This marker provided robust support for the suborders Cotylea and Acotylea and good resolution for the Cotylean superfamilies Periceloidea (BS = 100), Prosthiostomoidea (BS = 99), and Pseudocerotoidea (BS = 98). The two largest Cotylean superfamilies (Prosthiostomoidea and Pseudocerotoidea) were grouped in a clade with a bootstrap support of 95. Within Acotylea, the superfamily Stylochoidea was not supported (BS = 75), and the superfamily Leptoplanoidea did not form a monophyletic assemblage. As a result, at the family and genus levels, 16S rRNA did not yield clear groups within the leptoplanoids.

cox1 (Fig. 4): This marker is considered the molecular “barcode” for the majority of species. In this study, support varied depending on the taxonomic level. At the suborder level, the support values were lower than those from other genes (BS = 77). At the next level, the mainly Cotylean and Acotylean superfamilies were recovered. The majority of families in both suborders did not form monophyletic clusters.

cytb (Fig. 4): Regarding the last of the studied markers, cytb separated the two suborders Cotylea and Acotylea (BS = 100). It also displayed high support for the Cotylean and Acotylean superfamilies. At family level, cytb provided good support in both suborders (Colylea: Euryleptidae BS = 86 and Pseudocerotidae BS = 99; Acotylea: Leptoplanidae BS = 98, Planoceridae BS = 77, Latocestidae BS = 82, and Stylochidae BS = 80), but the majority of Cotylean and Acotylean superfamilies were not recovered (Fig. 4).

All assessed markers placed Cestoplana within or as the sister lineage of Cotylea, but none showed an unequivocal phylogenetic or kinship relationship between Cestoplana rubrocincta and the other taxa.

Discussion

This study compares, for the first time, the substitutions of mitochondrial and nuclear molecular markers at the order level for polyclad flatworms, including representatives of all superfamilies within the suborders Cotylea and Acotylea.

Regarding entropy values, it is worth noting the small proportion of variable sites in the 18S rDNA nuclear gene that denote low phylogenetic values in our analyses of the order compared to 28S rDNA, which presented regions with clear variability alternating with conserved regions (Fig. 1A). This difference is made more apparent when compared to the studied mitochondrial markers (16S rDNA, cox1, and cytb), which all presented high variability and substitution rates. In addition, in the three mitochondrial markers, variability was present throughout the entire DNA sequence, which is possibly one of the reasons for the difficulty in creating generic primers for these species, especially in the case of cox1. In most invertebrate taxa, cox1 sequencing is possible using universal primers such as those designed by Folmer and co-workers (Folmer et al. 1994) or, more recently, Lobo and colleagues (Lobo et al. 2013). For some taxonomic groups, however, these markers do not hybridise, and this appears to be the case for most Rhabditophora (Platyhelminthes), including those in the order Polycladida (mainly in the suborder Cotylea). In this situation, specific primers are frequently required (Aguado et al. 2017; Oya et al. 2019; Cuadrado et al. 2021).

The absolute values of substitution rates observed in our research reflect a linear increase in variability in all cases. A decrease in the absolute mutation rate was only observed in cytb, which may have been caused by a certain saturation in the signal of sequence substitution due to multiple recurrent changes since more than 80% of each sequence displayed variability. This saturation trend could lead to underestimating the variation in determinate terminal taxa. Therefore, it would be more advisable to use this marker for conducting phylogenetic analyses of closer groups, such as families or superfamilies.

The Ti/Tv ratio remained relatively stable for most cases, except for 28S rDNA, which presented a higher number of transitions at all distances, and cytb, which displayed a higher number of transversions. In the case of the 28S ribosomal gene, the elevated number of transitions is most likely due to a lack of conservation of the secondary structure of the RNA molecule (Rivas 2021), which may be required to preserve its function, with stem structures forming at transitions where needed.

In contrast, the overall increase in transversions in mitochondrial genes, particularly in cytb, could be the accumulation of substitutions when comparing variable sequences very distant from each other. All four types of transitions, as opposed to eight types of transversions, need to be considered in such situations. Previous studies have suggested that, compared with non-synonymous transversions, non-synonymous transitions are less deleterious because they tend not to cause radical changes in amino acid physicochemical properties such as charge, polarity, and size (e.g., Zhang 2000). However, our research shows a higher proportion of transitions were observed for the 28S rDNA nuclear gene in comparison to the mitochondrial markers. Meanwhile, 18S rDNA presented so few changes that it could hardly indicate a tendency in the analyses. Our results appear to validate the proposition made by Zou and Zhang (2021), who stated that the Ti/Tv ratio can be more or less than 1 (i.e., transversions or transitions being more prevalent) depending on the group studied. They attributed variations between interchangeable amino acids in protein-coding genes as a possible cause for this (e.g., variations in the genetic code of different taxa, differences in the functionality of generated proteins, etc.). In the case of the phylum Platyhelminthes, it is important to point out that the flatworm mitochondrial genetic code possesses four variations compared to the standard invertebrate mitochondrial code. For example, AAA codifies for asparagine (Asn) in flatworms, while in the standard mitochondrial code only AAT and AAC codify for this amino acid, which leads to fixating a transversion in this group. Likewise, the codons AGA and AGG translate to serine (Ser) in the flatworm mitochondrial genetic code instead of Arg, fixating two additional transversions, and UGA codifies for Trp rather than being a stop codon (Telford et al. 2000).

Different patterns of substitutions were also observed for the results of the 28S rDNA nuclear gene (Fig. 1B) and those present in the mitochondrial genes when comparing transition and transversion rates. The number of transitions surpasses the number of transversions in the 28S rDNA, while the mitochondrial markers show more transversions than transitions. The variations in the mitochondrial genetic code of flatworms mentioned earlier could lead to a higher chance of fixating transversions. Because of this, we suggest a more exhaustive study on this increase in transversions in the mitochondrial DNA of polyclads and its implications for Platyhelminthes more generally.

Conspicuous differences were observed when comparing all codon positions of each of the studied protein-codifying genes (cytb and cox1). Saturation of the transversions was observed in the third codon position of cox1. Such saturation has been reported previously for other taxonomic groups such as triclads (Alvarez-Presas et al. 2008), protists (Liu and Zhang 2019), insects (Schrider et al. 2013), plants (Ossowski et al. 2010), and nematodes (Denver et al. 2009). It is possible that the decrease in the signal of the number of transitions at distances higher than 25% observed in our research could lead to errors in phylogenetic analyses of polyclad flatworms when using the cox1 genetic marker. A plausible solution to reduce this effect during future phylogenetic analyses would be to delete the third codon position from the alignment. The effectiveness of this, however, is beyond the scope of this study.

Based on the results obtained in the ML analysis (Figs 3, 4), the markers with the best clade support and agreement with morphological relationships by histological analyses (Faubel 1983, 1984) were 28S rDNA (nuclear) and the mitochondrial markers 16S rDNA and cytb. 18S rDNA did not offer strong support at any taxonomic level studied. Moreover, substantial differences in support existed within the effective and resolving markers: 28S rDNA (nuclear) and 16S rDNA/cytb (mitochondrial). The differences observed between mitochondrial and nuclear markers, along with their potential incongruences in phylogenetic analyses, have previously been documented in other taxa, including Anthozoa, Insecta, and mammals (Zadra et al. 2021; Fedorov et al. 2022; Quattrini et al. 2023).

28S rDNA resolved the majority of nodes well for the systematics of suborder, superfamily, family, and, in some cases, at the genus and species level in Cotylea. Nevertheless, the 28S rDNA proved less effective in resolving deep nodes within Acotylea, resulting in the formation of paraphyletic nodes.

Within the mitochondrial markers, the best resolution level (>75 bootstrap support), compared to current phylogeny (Goodheart et al. 2023), was observed at the genus and species level. Both 16S rDNA and cytb strengthened and delimited the genera, resolving specific clusters within Cotylea and Acotylea. The specific combinations presented in our analyses revealed differences, such as the relationship between Cestoplana and Pericelis within Cotylea or Echinoplana with Leptoplana in Acotylea, although these relationships were not recovered by cytb. This may be caused by the increased substitution rate present among distantly related taxa within the phylogenetic tree (resulting in decreased linear progression of R2) and a more complex evolutionary history for these genes or taxa.

Conclusion

Among the tested markers, cytb presented a higher rate of variability and did not show saturation of transitions for any codon position. Moreover, this marker presented the highest range of distances (0% to 34.40%), with an average distance of 26.86% compared to that of cox1 (highest range of distances: 0.22% to 34.44%, average distance: 24.86%).

The use of a common marker for the order Polycladida would allow direct phylogenetic comparison across studies. General primers for these mitochondrial genes often fail to hybridise, so we also recommend designing de novo cox1-specific primers for families within the suborder Cotylea and cytb-specific primers for those within Acotylea, taking into consideration third base positions. The de novo design markers will allow amplification of cox1 and cytb sequences for certain groups of polyclad flatworms that previously could not be analysed due to the high number of substitutions across the whole sequence and the lack of conserved regions.

Thus, for polyclad flatworms, we conclude that for future studies at the order level, we encourage the use of mitochondrial genes cytb and 16S rDNA and nuclear ribosomal genes 28S rDNA. We also encourage the use of the cox1 gene with the caution of analysing the third codon position to avoid errors in the analyses and resolution of deep nodes at a generic or specific level. Certainly, the most crucial aspect is to determine the specific research inquiry and taxonomic level (such as order, family, or genus) and consequently select the appropriate genes to better address the study. In the present study, we analysed five markers currently used in the resolution of phylogenies, kinship analysis, delimitation of species, etc. We look forward to future polyclad studies using our suggested approach so that we can continue advancing the systematics and origin of this taxon on a global scale. New sequencing techniques offer the possibility of incorporating additional molecular information if the selected genes accurately represent the evolutionary history of the species. Concatenating data from different suitable markers will further bolster support for the analysed clusters.

Our case study highlights the need to evaluate how well nuclear and mitochondrial genes perform within a specific taxonomic group level. We propose that the use of transition bias is a useful tool for distinguishing which markers may be more effective for any taxon and could help streamline success for future systematic studies. It would also make cross-study evaluation within a taxonomic group more effective. A more globally collaborative approach to molecular systematics would certainly facilitate the use of this approach.

Acknowledgements

We thank the Linnean Society of New South Wales for their funding via a Vickery Fund Research Grant. The authors thank the School of Natural Sciences at Macquarie University for their institutional and financial support, the Australian Museum Research Institute, and the members of the Marine Invertebrates and Malacology Departments for providing access to their facilities and laboratories and assisting in fieldwork. Thanks to Audrey Falconer, Leon Altoff, and the members of the Field Naturalists’ Club of Victoria for their assistance in collecting samples and financial support through the FNCV Environment Fund. We extend our gratitude to the members of the Marine Ecology Group from Macquarie University (Justin McNab, Louise Tosetto, Patrick Burke, and Ryan Nevatte) for their help during fieldwork. F.Á.F.-Á. was supported by a Beatriu de Pinós fellowship from the Secretaria d’Universitats i Recerca del Departament de Recerca i Universitats of the Generalitat de Catalunya (Ref. BP 2021 00035). This research was also supported by the Spanish government through the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S). Lastly, J.R. expresses his gratitude to the Australian Government and Macquarie University for funding his livelihood and research through the International Research Training Programme (iRTP) Scholarship.

References

  • Aguado MT, Grande C, Gerth M, Bleidorn C, Noreña C (2016) Characterization of the complete mitochondrial genomes from Polycladida (Platyhelminthes) using next-generation sequencing. Gene 575(2): 199–205. https://doi.org/10.1016/j.gene.2015.08.054
  • Aguado MT, Noreña C, Alcaraz L, Marquina D, Brusa F, Almon B, Bleidorn C, Grande C (2017) Phylogeny of Polycladida (Platyhelminthes) based on mtDNA data. Organisms, Diversity & Evolution 17(4): 767–778. https://doi.org/10.1007/s13127-017-0344-4
  • Alvarez-Presas M, Baguna J, Riutort M (2008) Molecular phylogeny of land and freshwater planarians (Tricladida, Platyhelminthes): From freshwater to land and back. Molecular Phylogenetics and Evolution 47(2): 555–568. https://doi.org/10.1016/j.ympev.2008.01.032
  • Bahia J (2016) First records of polyclads (Platyhelminthes, Polycladida) associated with Nodipecten nodosus (Linnaeus 1758) aquaculture. Marine Biodiversity 46(4): 911–915. https://doi.org/10.1007/s12526-015-0425-6
  • Bahia J, Padula V, Schrödl M (2017) Polycladida phylogeny and evolution: Integrating evidence from 28S rDNA and morphology. Organisms, Diversity & Evolution 17(3): 653–678. https://doi.org/10.1007/s13127-017-0327-5
  • Broughton RE, Stanley SE, Durrett RT (2000) Quantification of homoplasy for nucleotide transitions and transversions and a reexamination of assumptions in weighted phylogenetic analysis. Systematic Biology 49(4): 617–627. https://doi.org/10.1080/106351500750049734
  • Carrera-Parra LF, Fauchald K, Gambi C (2011) Revision of the taxonomic status of Lysidice (Polychaeta, Eunicidae) in the western Caribbean Sea with observation on species reproductive features and habitat preference. The Italian Journal of Zoology 78(Suppl. 1): 27–40. https://doi.org/10.1080/11250003.2011.593850
  • Cuadrado D, Rodríguez J, Moro L, Grande C, Noreña C (2021) Polycladida (Platyhelminthes, Rhabditophora) from Cape Verde and related regions of Macaronesia. European Journal of Taxonomy 736: 1–43. https://doi.org/10.5852/ejt.2021.736.1249
  • Denver DR, Dolan PC, Wilhelm LJ, Sung W, Lucas-Lledo JI, Howe DK, Lewis SC, Okamoto K, Thomas WK, Lynch M, Baer CF (2009) A genome-wide view of Caenorhabditis elegans base-substitution mutation processes. Proceedings of the National Academy of Sciences of the United States of America 106(38): 16310–16314. https://doi.org/10.1073/pnas.0904895106
  • Faubel A (1983) The Polycladida, Turbellaria; Proposal and establishment of a new system. Part I. The Acotylea. Mitteilungen aus dem Hamburgischen Zoologischen Museum und Institut 80: 17–121.
  • Faubel A (1984) The Polycladida, Turbellaria; Proposal and establishment of a new system. Part II. The Cotylea. Mitteilungen aus dem Hamburgischen Zoologischen Museum und Institut 81: 189–259.
  • Fedorov VB, Trucchi E, Goropashnaya AV, Chr Stenseth N (2022) Conflicting nuclear and mitogenome phylogenies reveal ancient mitochondrial replacement between two North American species of collared lemmings (Dicrostonyx groenlandicus, D. hudsonius). Molecular Phylogenetics and Evolution 168: 107399. https://doi.org/10.1016/j.ympev.2022.107399
  • Fernández-Álvarez FÁ, Braid HE, Nigmatullin CM, Bolstad KSR, Haimovici M, Sánchez P, Sajikumar KK, Ragesh N, Villanueva R (2020) Global biodiversity of the genus Ommastrephes d’Orbigny, 1834 (Ommastrephidae: Cephalopoda): an allopatric cryptic species complex. Zoological Journal of the Linnean Society 190(2): 460–482. https://doi.org/10.1093/zoolinnean/zlaa014
  • Folmer O, Black M, Hoeh W, Lutz R, Vrijenhoek R (1994) DNA primers for amplification of mitochondrial cytochrome c oxidase subunit I from diverse metazoan invertebrates. Molecular Marine Biology and Biotechnology 3: 294–299.
  • Forsman ZH, Barshis DJ, Hunter CL, Toonen RJ (2009) Shape-shifting corals: Molecular markers show morphology is evolutionarily plastic in Porites. BMC Evolutionary Biology 9(1): 1–9. https://doi.org/10.1186/1471-2148-9-45
  • Gammoudi M, Ahmed RB, Bouriga N, Ben-Attia M, Harrath AH (2016) Predation by the polyclad flatworm Imogine mediterranea on the cultivated mussel Mytilus galloprovincialis in Bizerta Lagoon (northern Tunisia). Aquaculture Research 1: 10. https://doi.org/10.1111/are.12995
  • Goodheart JA, Collins AG, Cummings MP, Egger B, Rawlinson KA (2023) A phylogenomic approach to resolving interrelationships of polyclad flatworms, with implications for life history evolution. Royal Society Open Science 10(3): 220939. https://doi.org/10.1098/rsos.220939
  • Hebert PDN, Cywinska A, Ball SL, deWaard JR (2003) Biological identifications through DNA barcodes. Proceedings. Biological Sciences 270(1512): 313–321. https://doi.org/10.1098/rspb.2002.2218
  • Kalyaanamoorthy S, Minh BQ, Wong TKF, von Haeseler A, Jermiin LS (2017) ModelFinder: Fast model selection for accurate phylogenetic estimates. Nature Methods 14(6): 587–589. https://doi.org/10.1038/nmeth.4285
  • Kearse M, Moir R, Wilson A, Stones-Havas S, Cheung M, Sturrock S, Buxton S, Cooper A, Markowitz S, Duran C, Thierer T, Ashton B, Meintjes P, Drummond A (2012) Geneious basic: An integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics 28(12): 1647–1649. https://doi.org/10.1093/bioinformatics/bts199
  • Kenny NJ, Noreña C, Damborenea C, Grande C (2019) Probing recalcitrant problems in polyclad evolution and systematics with novel mitochondrial genome resources. Genomics 111(3): 343–355. https://doi.org/10.1016/j.ygeno.2018.02.009
  • Kimura M (1980) A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences. Journal of Molecular Evolution 16(2): 111–120. https://doi.org/10.1007/BF01731581
  • Kupriyanova EK, ten Hove HA, Rouse GW (2023) Phylogenetic relationships of Serpulidae (Annelida, Polychaeta) inferred from morphology and molecular data: Re-classification of Serpulidae. Diversity 15(3): 398. https://doi.org/10.3390/d15030398
  • Larkin MA, Blackshields G, Brown NP, Chenna R, McGettigan PA, McWilliam H, Valentin F, Wallace IM, Wilm A, Lopez R, Thompson JD, Gibson TJ, Higgins DG (2007) Clustal W and Clustal X version 2.0. Bioinformatics 23(21): 2947–2948. https://doi.org/10.1093/bioinformatics/btm404
  • Letunic I, Bork P (2019) Interactive Tree of Life (iTOL) v4: Recent updates and new developments. Nucleic Acids Research 47(W1): W256–W259. https://doi.org/10.1093/nar/gkz239
  • Litvaitis MK, Newman LJ (2001) A molecular framework for the phylogeny of the Pseudocerotidae (Platyhelminthes, Polycladida). Hydrobiologia 444(1–3): 177–182. https://doi.org/10.1023/A:1017503124908
  • Litvaitis MK, Bolaños DM, Quiroga SY (2010) When names are wrong and colours deceive: unravelling the species complex (Turbellaria: Polycladida). Journal of Natural History 44(13–14): 829–845. https://doi.org/10.1080/00222930903537074
  • Litvaitis MK, Bolaños DM, Quiroga SY (2019) Systematic congruence in Polycladida (Platyhelminthes, Rhabditophora): Are DNA and morphology telling the same story? Zoological Journal of the Linnean Society 186(4): 865–891. https://doi.org/10.1093/zoolinnean/zlz007
  • Lobo J, Costa PM, Teixeira MA, Ferreira MSG, Costa MH, Filipe O, Costa FO (2013) Enhanced primers for amplification of DNA barcodes from a broad range of marine metazoans. BMC Ecology 13: 34. https://doi.org/10.1186/1472-6785-13-34
  • Marquina D, Fernández-Álvarez FA, Noreña C (2015a) Five new records and one new species of Polycladida (Platyhelminthes) for the Cantabrian coast (North Atlantic) of the Iberian Peninsula. Journal of the Marine Biological Association of the United Kingdom 95(2): 311–322. https://doi.org/10.1017/S0025315414001106
  • Marquina D, Aguado MT, Noreña C (2015b) New records of Cotylea (Polycladida, Platyhelminthes) and one new species from Lizard Island (Australia), with remarks on the distribution of the Pseudoceros Lang, 1884 and Pseudobiceros Faubel, 1984 species of the Indo-Pacific marine region. Zootaxa 4019(1): 354–377. https://doi.org/10.11646/zootaxa.4019.1.14
  • McNab JM, Briggs MT, Williamson JE, Hoffmann P, Rodríguez J, Karuso P (2022) Structural characterization and spatial mapping of tetrodotoxins in Australian polyclads. Marine Drugs 20(12): 788. https://doi.org/10.3390/md20120788
  • Noreña C, Marquina D, Perez J, Almon B (2014) First records of Cotylea (Polycladida, Platyhelminthes) for the Atlantic coast of the Iberian Peninsula. ZooKeys 404: 1–22. https://doi.org/10.3897/zookeys.404.7122
  • Noreña C, Rodríguez J, Pérez J, Almon B (2015) New Acotylea (Polycladida, Platyhelminthes) from the east coast of the North Atlantic Ocean with special mention of the Iberian littoral. Zootaxa 4039(1): 157–172. https://doi.org/10.11646/zootaxa.4039.1.7
  • Oliver T, Isaac N, August T, Woodcock BW, David BR, Bullock JM (2015) Declining resilience of ecosystem functions under biodiversity loss. Nature Communications 6(1): 10122. https://doi.org/10.1038/ncomms10122
  • Ossowski S, Schneeberger K, Lucas-Lledo JI, Warthmann N, Clark RM, Shaw RG, Weigel D, Lynch M (2010) The rate and molecular spectrum of spontaneous mutations in Arabidopsis thaliana. Science 327(5961): 92–94. https://doi.org/10.1126/science.1180677
  • Oya Y, Kajihara H (2017) Description of a new Notocomplana species (Platyhelminthes: Acotylea), new combination and new records of Polycladida from the northeastern Sea of Japan, with a comparison of two different barcoding markers. Zootaxa 4282(3): 526–542. https://doi.org/10.11646/zootaxa.4282.3.6
  • Oya Y, Kajihara H (2020) Molecular Phylogenetic Analysis of Acotylea (Platyhelminthes: Polycladida). Zoological Science 37(3): 271–279. https://doi.org/10.2108/zs190136
  • Oya Y, Kimura T, Kajihara H (2019) Description of a new species of Paraplehnia (Polycladida, Stylochoidea) from Japan, with inference on the phylogenetic position of Plehniidae. ZooKeys 864: 1–13. https://doi.org/10.3897/zookeys.864.33955
  • Park JM, Powell NN, Gillings MR, Gaston TF, Williamson JE (2020) Phylogeny and form in fishes: Genetic and morphometric characteristics of dragonets (Foetorepus sp.) do not align. Acta Zoologica (Stockholm, Sweden) 101(2): 218–226. https://doi.org/10.1111/azo.12287
  • Pérez-García P, Noreña C, Cervera JL (2019) Two new acotylean flatworms (Polycladida) of two genera unrecorded in the Eastern Atlantic. Marine Biodiversity 49(3): 1187–1195. https://doi.org/10.1007/s12526-018-0900-y
  • Prudhoe S (1985) A monograph on Polyclad Turbellaria. London/Oxford: British Museum of Natural History and Oxford University Press.
  • Quattrini AM, Snyder KE, Purow-Ruderman R, Seiblitz IGL, Hoang J, Floerke N, Ramos NI, Wirshing HH, Rodriguez E, McFadden CS (2023) Mito-nuclear discordance within Anthozoa, with notes on unique properties of their mitochondrial genomes. Scientific Reports 13(1): 7443. https://doi.org/10.1038/s41598-023-34059-1
  • Raupach MJ, Mayer C, Malyutina M, Wagele JW (2009) Multiple origins of deep-sea Asellota (Crustacea: Isopoda) from shallow waters revealed by molecular data. Proceedings of the Royal Society B, Biological Sciences 276(1658): 799–808. https://doi.org/10.1098/rspb.2008.1063
  • Rawlinson KA, Stella JS (2012) Discovery of the Corallivorous Polyclad flatworm, Amakusaplana acroporae, on the Great Barrier Reef, Australia – the first report from the wild. PLOS ONE 7(8): e42240. https://doi.org/10.1371/journal.pone.0042240
  • Rawlinson KA, Gillis JA, Billings Jr RE, Borneman EH (2011) Taxonomy and life history of the Acropora-eating flatworm Amakusaplana acroporae nov. sp. (Polycladida: Prosthiostomidae). Coral Reefs 30(3): 693. https://doi.org/10.1007/s00338-011-0745-3
  • Rodríguez J, Hutchings P, Williamson JE (2021) Biodiversity of intertidal marine flatworms (Polycladida, Platyhelminthes) in southeastern Australia. Zootaxa 5024(1): 1–63. https://doi.org/10.11646/zootaxa.5024.1.1
  • Soutullo P, Cuadrado D, Noreña C (2021) First study of the Polycladida (Rhabditophora, Platyhelminthes) from the Pacific Coast of Costa Rica. Zootaxa 4964(2): 363–381. https://doi.org/10.11646/zootaxa.4964.2.7
  • Strand M, Sundberg P (2005) Delimiting species in the hoplonemertean genus Tetrastemma (phylum Nemertea): Morphology is not concordant with phylogeny as evidenced from mtDNA sequences. Biological Journal of the Linnean Society, Linnean Society of London 86(2): 201–212. https://doi.org/10.1111/j.1095-8312.2005.00535.x
  • Swofford DL (2003) PAUP*. Phylogenetic Analysis Using Parsimony (*and Other Methods). Version 4. Sinauer Associates, Sunderland, Massachusetts.
  • Tamura K, Nei M (1993) Estimation of the number of nucleotide substitutions in the control region of mitochondrial DNA in humans and chimpanzees. Molecular Biology and Evolution 10(3): 512–526.
  • Tamura K, Stecher G, Peterson D, Filipski A, Kumar S (2013) MEGA6: Molecular evolutionary genetics analysis version 6.0. Molecular Biology and Evolution 30(12): 2725–2729. https://doi.org/10.1093/molbev/mst197
  • Telford MJ, Herniou EA, Russell RB, Littlewood DTJ (2000) Changes in mitochondrial genetic codes as phylogenetic characters: Two examples from the flatworms. Proceedings of the National Academy of Sciences of the United States of America 97(21): 11359–11364. https://doi.org/10.1073/pnas.97.21.11359
  • Tosetto L, McNab JM, Hutchings PA, Rodríguez J, Williamson JE (2023) Fantastic flatworms and where to find them: Insights into intertidal polyclad flatworm distribution in southeastern Australian boulder beaches. Diversity 15(3): 393. https://doi.org/10.3390/d15030393
  • Trifinopoulos J, Nguyen LT, von Haeseler A, Minh BQ (2016) W-IQ-TREE: A fast online phylogenetic tool for maximum likelihood analysis. Nucleic Acids Research 44(W1): 232–235. https://doi.org/10.1093/nar/gkw256
  • Tsunashima T, Hagiya M, Yamada R, Koito T, Tsuyuk N, Izawa S, Kosoba K, Itoi S, Sugita H (2017) A molecular framework for the taxonomy and systematics of Japanese marine turbellarian flatworms (Platyhelminthes, Polycladida). Aquatic Biology 26: 159–167. https://doi.org/10.3354/ab00682
  • Tsuyuki A, Oya Y, Kajihara H (2019) A New Species of Prosthiostomum (Platyhelminthes: Polycladida) from Shirahama, Japan. Species Diversity : An International Journal for Taxonomy, Systematics, Speciation, Biogeography, and Life History Research of Animals 24(2): 137–143. https://doi.org/10.12782/specdiv.24.137
  • Tsuyuki A, Oya Y, Kajihara H (2022) Two new species of the marine flatworm Pericelis (Platyhelminthes: Polycladida) from southwestern Japan with an amendment of the generic diagnosis based on phylogenetic inference. Marine Biology Research. https://doi.org/10.1080/17451000.2022.2048669
  • Valdés A, Breslau E, Padula V, Schrödl M, Camacho Y, Malaquias MA, Alexander J, Bottomley M, Vital XG, Hooker Y, Gosliner TM (2017) Molecular and morphological systematics of Dolabrifera Gray, 1847 (Mollusca: Gastropoda: Heterobranchia: Aplysiomorpha). Zoological Journal of the Linnean Society 184(1): 31–65. https://doi.org/10.1093/zoolinnean/zlx099
  • Vella A, Vella N, Maslin M, Bichlmaier L (2016) First molecular barcoding and record of the Indo-Pacific punctuated flatworm Maritigrella fuscopunctata (Newman and Cannon 2000), (Polycladida: Euryleptidae) from the Mediterranean Sea. Journal of the Black Sea/Mediterranean Environment 22: 119–127.
  • Xia X (2013) DAMBE5: A comprehensive software package for data analysis in molecular biology and evolution. Molecular Biology and Evolution 30(7): 1720–1728. https://doi.org/10.1093/molbev/mst064
  • Yonezawa R, Itoi S, Igarashi Y, Yoshitake K, Oyama H, Kinoshita S, Suo R, Yokobori S, Sugita H, Asakawa S (2020) Characterization and phylogenetic position of two sympatric sister species of toxic flatworms Planocera multitentaculata and Planocera reticulata (Platyhelminthes: Acotylea). Mitochondrial DNA, Part B, Resources 5(3): 2352–2354. https://doi.org/10.1080/23802359.2020.1730255
  • Zadra N, Rizzoli A, Rota-Stabelli O (2021) Chronological Incongruences between Mitochondrial and Nuclear Phylogenies of Aedes Mosquitoes. Life 11(3): 181. https://doi.org/10.3390/life11030181
  • Zhang J (2000) Rates of conservative and radical nonsynonymous nucleotide substitutions in mammalian nuclear genes. Journal of Molecular Evolution 50(1): 56–68. https://doi.org/10.1007/s002399910007
  • Zhang YM, Egan SP, Driscoe AL, Ott JR (2021) One hundred and sixty years of taxonomic confusion resolved: (Hymenoptera: Cynipidae: Cynipini) gall wasps associated with live oaks in the USA. Zoological Journal of the Linnean Society 193(4): 1234–1255. https://doi.org/10.1093/zoolinnean/zlab001
  • Zou Z, Zhang J (2021) Are nonsynonymous transversions generally more deleterious than nonsynonymous transitions? Molecular Biology and Evolution 38(1): 181–191. https://doi.org/10.1093/molbev/msaa200
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